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from numpy import ndarray
from torch import Tensor, device
from tqdm import tqdm
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from TorchJaekwon.GetModule import GetModule
from TorchJaekwon.Util.UtilData import UtilData
from TorchJaekwon.Util.UtilTorch import UtilTorch
from TorchJaekwon.Model.Diffusion.DDPM.DiffusionUtil import DiffusionUtil
from TorchJaekwon.Model.Diffusion.DDPM.BetaSchedule import BetaSchedule
class DDPM(nn.Module):
def __init__(self,
model_class_name:Optional[str] = None,
model:Optional[nn.Module] = None,
model_output_type:Literal['noise', 'x_start', 'v_prediction'] = 'noise',
timesteps:int = 1000,
loss_func:Union[nn.Module, Callable, Tuple[str,str]] = F.mse_loss, # if tuple (package name, func name). ex) (torch.nn.functional, mse_loss)
betas: Optional[ndarray] = None,
beta_schedule_type:Literal['linear','cosine'] = 'cosine',
beta_arg_dict:dict = dict(),
unconditional_prob:float = 0, #if unconditional_prob > 0, this model works as classifier free guidance
cfg_scale:Optional[float] = None # classifer free guidance scale
) -> None:
super().__init__()
if model_class_name is not None:
self.model = GetModule.get_model(model_name = model_class_name)
else:
self.model:nn.Module = model
self.model_output_type:Literal['noise', 'x_start', 'v_prediction'] = model_output_type
self.loss_func:Union[nn.Module, Callable] = loss_func
self.timesteps:int = timesteps
self.set_noise_schedule(betas=betas, beta_schedule_type=beta_schedule_type, beta_arg_dict=beta_arg_dict, timesteps=timesteps)
self.unconditional_prob:float = unconditional_prob
self.cfg_scale:Optional[float] = cfg_scale
def set_noise_schedule(self,
betas: Optional[ndarray] = None,
beta_schedule_type:Literal['linear','cosine'] = 'linear',
beta_arg_dict:dict = dict(),
timesteps:int = 1000,
) -> None:
if betas is None:
beta_arg_dict.update({'timesteps':timesteps})
betas = getattr(BetaSchedule,beta_schedule_type)(**beta_arg_dict)
alphas:ndarray = 1. - betas
alphas_cumprod:ndarray = np.cumprod(alphas, axis=0)
alphas_cumprod_prev:ndarray = np.append(1., alphas_cumprod[:-1])
self.betas:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'betas', value = betas)
self.alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'alphas_cumprod', value = alphas_cumprod)
self.alphas_cumprod_prev:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'alphas_cumprod_prev', value = alphas_cumprod_prev)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_alphas_cumprod', value = np.sqrt(alphas_cumprod))
self.sqrt_one_minus_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_one_minus_alphas_cumprod', value = np.sqrt(1. - alphas_cumprod))
self.log_one_minus_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'log_one_minus_alphas_cumprod', value = np.log(1. - alphas_cumprod))
self.sqrt_recip_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_recip_alphas_cumprod', value = np.sqrt(1. / alphas_cumprod))
self.sqrt_recipm1_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_recipm1_alphas_cumprod', value = np.sqrt(1. / alphas_cumprod - 1))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.posterior_variance:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_variance', value = posterior_variance)
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.posterior_log_variance_clipped:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_log_variance_clipped', value = np.log(np.maximum(posterior_variance, 1e-20)))
self.posterior_mean_coef1:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_mean_coef1', value = betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
self.posterior_mean_coef2:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_mean_coef2', value = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))
def forward(self,
x_start:Optional[Tensor] = None,
x_shape:Optional[tuple] = None,
cond:Optional[Union[dict,Tensor]] = None,
is_cond_unpack:bool = False,
stage: Literal['train', 'infer'] = 'train'
) -> Tensor: # return loss value or sample
'''
train diffusion model.
return diffusion loss
'''
x_start, cond, additional_data_dict = self.preprocess(x_start, cond)
if stage == 'train' and x_start is not None:
if x_shape is None: x_shape = x_start.shape
batch_size:int = x_shape[0]
input_device:device = x_start.device
t:Tensor = torch.randint(0, self.timesteps, (batch_size,), device=input_device).long()
if DDPM.make_decision(self.unconditional_prob):
cond:Optional[Union[dict,Tensor]] = self.get_unconditional_condition(cond=cond, condition_device=input_device)
return self.p_losses(x_start, cond, is_cond_unpack, t)
else:
return self.infer(x_shape = x_shape, cond = cond, is_cond_unpack = is_cond_unpack, additional_data_dict = additional_data_dict)
def p_losses(self,
x_start:Tensor,
cond:Optional[Union[dict,Tensor]],
is_cond_unpack:bool,
t:Tensor,
noise:Optional[Tensor] = None):
noise:Tensor = UtilData.default(noise, lambda: torch.randn_like(x_start))
x_noisy:Tensor = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output:Tensor = self.apply_model(x_noisy, t, cond, is_cond_unpack)
if self.model_output_type == 'x_start':
target:Tensor = x_start
elif self.model_output_type == 'noise':
target:Tensor = noise
elif self.model_output_type == 'v_prediction':
target:Tensor = self.get_v(x_start, noise, t)
else:
print(f'''model output type is {self.model_output_type}. It should be in [x_start, noise]''')
raise NotImplementedError()
if target.shape != model_output.shape: print(f'warning: target shape({target.shape}) and model shape({model_output.shape}) are different')
return self.loss_func(target, model_output)
def get_v(self, x, noise, t):
'''
Progressive Distillation for Fast Sampling of Diffusion Models
https://arxiv.org/abs/2202.00512
'''
return (
DiffusionUtil.extract(self.sqrt_alphas_cumprod, t, x.shape) * noise
- DiffusionUtil.extract(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
)
def q_sample(self, x_start:Tensor, t:Tensor, noise=None) -> Tensor:
'''
noisy x sample for forward process
'''
noise = UtilData.default(noise, lambda: torch.randn_like(x_start))
return (
DiffusionUtil.extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
DiffusionUtil.extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
)
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = DiffusionUtil.extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = DiffusionUtil.extract(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = DiffusionUtil.extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
@torch.no_grad()
def infer(self,
x_shape:tuple,
cond:Optional[Union[dict,Tensor]],
is_cond_unpack:bool,
additional_data_dict:dict):
if x_shape is None: x_shape = self.get_x_shape(cond)
model_device:device = UtilTorch.get_model_device(self.model)
x:Tensor = torch.randn(x_shape, device = model_device)
for i in tqdm(reversed(range(0, self.timesteps)), desc='sample time step', total=self.timesteps):
x = self.p_sample(x = x, t = torch.full((x_shape[0],), i, device= model_device, dtype=torch.long), cond = cond, is_cond_unpack = is_cond_unpack)
return self.postprocess(x, additional_data_dict = additional_data_dict)
@torch.no_grad()
def p_sample(self,
x:Tensor,
t:Tensor,
cond:Optional[Union[dict,Tensor]],
is_cond_unpack:bool,
clip_denoised:bool = False, # dangerous if True
repeat_noise:bool = False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, cond = cond, is_cond_unpack = is_cond_unpack, clip_denoised = clip_denoised)
noise = DiffusionUtil.noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
def p_mean_variance(self,
x:Tensor,
t:Tensor,
cond:Optional[Union[dict,Tensor]],
is_cond_unpack:bool,
clip_denoised: bool) -> Tuple[Tensor]:
model_output:Tensor = self.apply_model(x, t, cond, is_cond_unpack, cfg_scale=self.cfg_scale)
if self.model_output_type == "noise":
x_recon = self.predict_x_start_from_noise(x, t=t, noise=model_output)
elif self.model_output_type == 'x_start':
x_recon = model_output
elif self.model_output_type == 'v_prediction':
x_recon = self.predict_x_start_from_v(x, t=t, v=model_output)
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
def predict_x_start_from_noise(self, x_t, t, noise):
return (
DiffusionUtil.extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
DiffusionUtil.extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def predict_x_start_from_v(self, x_t, t, v):
# self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
# self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
return (
DiffusionUtil.extract(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t
- DiffusionUtil.extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_noise_from_v(self, x_t, t, v):
return (
DiffusionUtil.extract(self.sqrt_alphas_cumprod, t, x_t.shape) * v
+ DiffusionUtil.extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape)
* x_t
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
DiffusionUtil.extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
DiffusionUtil.extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = DiffusionUtil.extract(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = DiffusionUtil.extract(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def preprocess(self, x_start:Tensor, cond:Optional[Union[dict,Tensor]] = None) -> Tuple[Tensor, Optional[Union[dict,Tensor]], dict]:
return x_start, cond, None
def postprocess(self, x:Tensor, additional_data_dict:dict) -> Tensor:
return x
def apply_model(self,
x:Tensor,
t:Tensor,
cond:Optional[Union[dict,Tensor]],
is_cond_unpack:bool,
cfg_scale:Optional[float] = None
) -> Tensor:
if cfg_scale is None or cfg_scale == 1.0:
if cond is None:
return self.model(x, t)
elif is_cond_unpack:
return self.model(x, t, **cond)
else:
return self.model(x, t, cond)
else:
model_conditioned_output = self.model(x, t, **cond) if is_cond_unpack else self.model(x, t, cond)
unconditional_conditioning = self.get_unconditional_condition(cond=cond)
model_unconditioned_output = self.model(x, t, **unconditional_conditioning) if is_cond_unpack else self.model(x, t, unconditional_conditioning)
return model_unconditioned_output + cfg_scale * (model_conditioned_output - model_unconditioned_output)
@staticmethod
def make_decision(probability:float #[0,1]
) -> bool:
if probability == 0:
return False
if float(torch.rand(1)) < probability:
return True
else:
return False
def get_unconditional_condition(self,
cond:Optional[Union[dict,Tensor]] = None,
cond_shape:Optional[tuple] = None,
condition_device:Optional[device] = None
) -> Tensor:
print('Default Unconditional Condition. You might wanna overwrite this function')
if cond_shape is None: cond_shape = cond.shape
if cond is not None and isinstance(cond,Tensor): condition_device = cond.device
return (-11.4981 + torch.zeros(cond_shape)).to(condition_device)
def get_x_shape(self, cond:Optional[Union[dict,Tensor]] = None):
return None
if __name__ == '__main__':
DDPM(model = 'debug')
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